Ensemble Machine Learning: A beginner’s guide that combines powerful machine learning algorithms to build optimized models
By 作者: Ankit Dixit
ISBN-10 书号: 178829775X
ISBN-13 书号: 9781788297752
Release Finelybook 出版日期: 2017-12-21
pages 页数: 438
Book Description to Finelybook sorting
Ensembling is a technique of combining two or more similar or dissimilar machine learning algorithms to create a model that delivers superior prediction power. This book will show you how you can use many weak algorithms to make a strong predictive model. This book contains Python code for different machine learning algorithms so that you can easily understand and implement it in your own systems.
This book covers different machine learning algorithms that are widely used in the practical world to make predictions and classifications. It addresses different aspects of a prediction framework, such as data pre-processing, model training, validation of the model, and more. You will gain knowledge of different machine learning aspects such as bagging (decision trees and random forests), Boosting (Ada-boost) and stacking (a combination of bagging and boosting algorithms).
Then you’ll learn how to implement them by building ensemble models using TensorFlow and Python libraries such as scikit-learn and NumPy. As machine learning touches almost every field of the digital world, you’ll see how these algorithms can be used in different applications such as computer vision, speech recognition, making recommendations, grouping and document classification, fitting regression on data, and more.
By the end of this book, you’ll understand how to combine machine learning algorithms to work behind the scenes and reduce challenges and common problems.
1: INTRODUCTION TO ENSEMBLE LEARNING
2: DECISION TREES
3: RANDOM FOREST
4: RANDOM SUBSPACE AND KNN BAGGING
5: ADABOOST CLASSIFIER
6: GRADIENT BOOSTING MACHINES
7: XGBOOST – EXTREME GRADIENT BOOSTING
8: STACKED GENERALIZATION
9: STACKED GENERALIZATION – PART 2
10: MODERN DAY MACHINE LEARNING
What You Will Learn
Understand why bagging improves classification and regression performance
Get to grips with implementing AdaBoost and different variants of this algorithm
See the bootstrap method and its application to bagging
Perform regression on Boston housing data using scikit-learn and NumPy
Know how to use Random forest for IRIS data classification
Get to grips with the classification of sonar dataset using KNN, Perceptron, and Logistic Regression
Discover how to improve prediction accuracy by fine-tuning the model parameters
Master the analysis of a trained predictive model for over-fitting/under-fitting cases
Ankit Dixit is a data scientist and computer vision engineer from Mumbai. Ankit has studied BTech in biomedical engineering and has a master’s degree in computer vision specialization. He has worked in the field of computer vision and machine learning for the past 6 years. He has worked with various software and hardware platforms for the design and development of machine vision algorithms. Ankit has experience with a wide variety of machine learning algorithms. Currently, he is focusing on designing computer vision and machine learning algorithms for medical imaging data, with the use of various advanced technologies such as ensemble methods and deep learning-based models.